Evaluate the Effects of An AI System on Colonoscopy Quality of Novice Endoscopists

Colonoscopy is a crucial technique for detecting and diagnosing lower digestive tract lesions. The demand for endoscopy is high in China, and endoscopy is in short supply. However, a colonoscopy is a complex technical procedure that requires training and experience for maximal accuracy and safety. The ability of different endoscopists varies greatly. Novice endoscopists generally have difficulty and high risk in entering colonoscopy, requiring experts' assistance. To some extent, this wastes the novice's productivity. If investigators can arrange the working mode of experts entering and novices withdrawing endoscopy, the clinical efficiency and resource utilization rate can be significantly improved. However, investigators must consider the poor examination ability of novice endoscopists. It is reported that the detection rate of adenoma in colonoscopy performed by endoscopists with different seniority is 7.4% ~ 52.5%. If the examination ability of novice endoscopists can be improved, this concern can be eliminated.

Deep learning algorithms have been continuously developed and increasingly mature in recent years. They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines to "see". Through deep learning, camera and computer can replace human eyes to carry out machine vision such as target recognition, tracking and measurement. Interdisciplinary cooperation in medical imaging and computer vision is also one of the research hotspots in recent years. At present, it is mainly applied to the automatic identification and detection of lesions and quality control and has achieved good results.

Investigator's preliminary experiments have shown that deep learning has high accuracy in endoscopic quality monitoring, which can effectively regulate doctors' operations, reduce blind spots and improve the quality of endoscopic examination. At the same time, it can also monitor the doctor's withdrawal time in real-time and improve the detection rate of adenoma. In the previous work of investigator's research group, investigators have successfully developed deep learning-based colonoscopy withdraw speed monitoring and intestinal cleanliness assessment and verified the effectiveness of the AI-assisted system EndoAngel in improving the quality of gastroscopy and colonoscopy in clinical trials.

Based on the above rich foundation of preliminary work and the massive demand for improving the colonoscopy ability of novices. By comparing the performance of novices and novices with EndoAngel assistance and experts in colonoscopy, investigators want to explore whether artificial intelligence can assist novices to reach the expert level in colonoscopy.

Inclusion criteria

• Male or female ≥18 years old;
• Able to read, understand and sign an informed consent;
• The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures;
• Patients requiring colonoscopy.

Exclusion criteria

• Have drug or alcohol abuse or mental disorder in the last 5 years;
• Pregnant or lactating women;
• Patients with known multiple polyp syndrome;
• patients with known inflammatory bowel disease;
• known intestinal stenosis or space-occupying tumor;
• known colon obstruction or perforation;
• patients with a history of colorectal surgery;
• Patients with a previous history of allergy to pre-used spasmolysis;
• Unable to perform biopsy and polyp removal due to coagulation disorders or oral anticoagulants;
• High-risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.